Overview

Brought to you by YData

Dataset statistics

Number of variables43
Number of observations26759
Missing cells18469
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.8 MiB
Average record size in memory1.6 KiB

Variable types

Numeric15
Text16
Categorical8
DateTime3
URL1

Alerts

alt is highly overall correlated with countryHigh correlation
circuitId is highly overall correlated with countryHigh correlation
country is highly overall correlated with alt and 3 other fieldsHigh correlation
driverId is highly overall correlated with raceId and 1 other fieldsHigh correlation
driver_nationality is highly overall correlated with driver_numberHigh correlation
driver_number is highly overall correlated with driver_nationalityHigh correlation
laps is highly overall correlated with positionOrderHigh correlation
lat is highly overall correlated with countryHigh correlation
lng is highly overall correlated with country and 1 other fieldsHigh correlation
points is highly overall correlated with positionOrder and 1 other fieldsHigh correlation
position is highly overall correlated with positionOrder and 1 other fieldsHigh correlation
positionOrder is highly overall correlated with laps and 4 other fieldsHigh correlation
positionText is highly overall correlated with position and 1 other fieldsHigh correlation
raceId is highly overall correlated with driverId and 1 other fieldsHigh correlation
resultId is highly overall correlated with driverId and 1 other fieldsHigh correlation
statusId is highly overall correlated with points and 1 other fieldsHigh correlation
time_y is highly overall correlated with lngHigh correlation
rank is highly imbalanced (50.3%) Imbalance
driver_number is highly imbalanced (63.7%) Imbalance
time_y is highly imbalanced (59.4%) Imbalance
race_datetime has 18469 (69.0%) missing values Missing
resultId is uniformly distributed Uniform
resultId has unique values Unique
grid has 1638 (6.1%) zeros Zeros
points has 18589 (69.5%) zeros Zeros
laps has 2532 (9.5%) zeros Zeros

Reproduction

Analysis started2025-08-20 18:20:05.998063
Analysis finished2025-08-20 18:20:19.253459
Duration13.26 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

resultId
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct26759
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13380.977
Minimum1
Maximum26764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:19.339452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1338.9
Q16690.5
median13380
Q320069.5
95-th percentile25426.1
Maximum26764
Range26763
Interquartile range (IQR)13379

Descriptive statistics

Standard deviation7726.1346
Coefficient of variation (CV)0.57739688
Kurtosis-1.199847
Mean13380.977
Median Absolute Deviation (MAD)6690
Skewness0.0003467618
Sum3.5806157 × 108
Variance59693157
MonotonicityNot monotonic
2025-08-20T23:50:19.400539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20025 1
 
< 0.1%
1960 1
 
< 0.1%
1932 1
 
< 0.1%
1931 1
 
< 0.1%
1930 1
 
< 0.1%
1929 1
 
< 0.1%
1928 1
 
< 0.1%
1927 1
 
< 0.1%
1926 1
 
< 0.1%
1925 1
 
< 0.1%
Other values (26749) 26749
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
26764 1
< 0.1%
26763 1
< 0.1%
26762 1
< 0.1%
26761 1
< 0.1%
26760 1
< 0.1%
26759 1
< 0.1%
26758 1
< 0.1%
26757 1
< 0.1%
26756 1
< 0.1%
26755 1
< 0.1%

raceId
Real number (ℝ)

High correlation 

Distinct1125
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551.68728
Minimum1
Maximum1144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:19.455363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile64
Q1300
median531
Q3811
95-th percentile1075
Maximum1144
Range1143
Interquartile range (IQR)511

Descriptive statistics

Standard deviation313.26504
Coefficient of variation (CV)0.56783081
Kurtosis-1.0593698
Mean551.68728
Median Absolute Deviation (MAD)254
Skewness0.12212825
Sum14762600
Variance98134.983
MonotonicityNot monotonic
2025-08-20T23:50:19.514904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800 55
 
0.2%
809 47
 
0.2%
367 39
 
0.1%
361 39
 
0.1%
371 39
 
0.1%
744 39
 
0.1%
368 39
 
0.1%
370 39
 
0.1%
357 39
 
0.1%
360 39
 
0.1%
Other values (1115) 26345
98.5%
ValueCountFrequency (%)
1 20
0.1%
2 20
0.1%
3 20
0.1%
4 20
0.1%
5 20
0.1%
6 20
0.1%
7 20
0.1%
8 20
0.1%
9 20
0.1%
10 20
0.1%
ValueCountFrequency (%)
1144 20
0.1%
1143 20
0.1%
1142 20
0.1%
1141 20
0.1%
1140 20
0.1%
1139 20
0.1%
1138 20
0.1%
1137 20
0.1%
1136 20
0.1%
1135 20
0.1%

driverId
Real number (ℝ)

High correlation 

Distinct861
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278.67353
Minimum1
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:19.577558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q157
median172
Q3399.5
95-th percentile840
Maximum862
Range861
Interquartile range (IQR)342.5

Descriptive statistics

Standard deviation282.70304
Coefficient of variation (CV)1.0144596
Kurtosis-0.38996547
Mean278.67353
Median Absolute Deviation (MAD)142
Skewness1.0208177
Sum7457025
Variance79921.009
MonotonicityNot monotonic
2025-08-20T23:50:19.633583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 404
 
1.5%
1 356
 
1.3%
8 352
 
1.3%
22 326
 
1.2%
18 309
 
1.2%
30 308
 
1.2%
20 300
 
1.1%
815 283
 
1.1%
13 271
 
1.0%
817 257
 
1.0%
Other values (851) 23593
88.2%
ValueCountFrequency (%)
1 356
1.3%
2 184
0.7%
3 206
0.8%
4 404
1.5%
5 112
 
0.4%
6 36
 
0.1%
7 27
 
0.1%
8 352
1.3%
9 99
 
0.4%
10 95
 
0.4%
ValueCountFrequency (%)
862 1
 
< 0.1%
861 9
 
< 0.1%
860 3
 
< 0.1%
859 11
 
< 0.1%
858 36
0.1%
857 46
0.2%
856 11
 
< 0.1%
855 68
0.3%
854 44
0.2%
853 22
 
0.1%

constructorId
Real number (ℝ)

Distinct211
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.180537
Minimum1
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:19.689108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median25
Q363
95-th percentile205
Maximum215
Range214
Interquartile range (IQR)57

Descriptive statistics

Standard deviation61.551498
Coefficient of variation (CV)1.226601
Kurtosis0.92464474
Mean50.180537
Median Absolute Deviation (MAD)20
Skewness1.4804598
Sum1342781
Variance3788.5869
MonotonicityNot monotonic
2025-08-20T23:50:19.744632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2439
 
9.1%
1 1923
 
7.2%
3 1676
 
6.3%
25 881
 
3.3%
32 871
 
3.3%
15 837
 
3.1%
9 788
 
2.9%
4 787
 
2.9%
18 672
 
2.5%
34 662
 
2.5%
Other values (201) 15223
56.9%
ValueCountFrequency (%)
1 1923
7.2%
2 140
 
0.5%
3 1676
6.3%
4 787
 
2.9%
5 536
 
2.0%
6 2439
9.1%
7 280
 
1.0%
8 78
 
0.3%
9 788
 
2.9%
10 424
 
1.6%
ValueCountFrequency (%)
215 48
 
0.2%
214 180
0.7%
213 166
0.6%
211 76
 
0.3%
210 380
1.4%
209 78
 
0.3%
208 154
0.6%
207 112
 
0.4%
206 109
 
0.4%
205 76
 
0.3%
Distinct130
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-20T23:50:19.833140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.6792855
Min length1

Characters and Unicode

Total characters44936
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.1%

Sample

1st row2
2nd row3
3rd row4
4th row14
5th row15
ValueCountFrequency (%)
4 1019
 
3.8%
16 1005
 
3.8%
11 1001
 
3.7%
6 994
 
3.7%
3 994
 
3.7%
8 993
 
3.7%
14 982
 
3.7%
10 976
 
3.6%
20 972
 
3.6%
2 959
 
3.6%
Other values (120) 16864
63.0%
2025-08-20T23:50:19.989182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11013
24.5%
2 9391
20.9%
3 4455
9.9%
4 3750
 
8.3%
6 3040
 
6.8%
5 2910
 
6.5%
7 2859
 
6.4%
8 2715
 
6.0%
0 2583
 
5.7%
9 2208
 
4.9%
Other values (2) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44924
> 99.9%
Other Punctuation 6
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11013
24.5%
2 9391
20.9%
3 4455
9.9%
4 3750
 
8.3%
6 3040
 
6.8%
5 2910
 
6.5%
7 2859
 
6.4%
8 2715
 
6.0%
0 2583
 
5.7%
9 2208
 
4.9%
Other Punctuation
ValueCountFrequency (%)
\ 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44930
> 99.9%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11013
24.5%
2 9391
20.9%
3 4455
9.9%
4 3750
 
8.3%
6 3040
 
6.8%
5 2910
 
6.5%
7 2859
 
6.4%
8 2715
 
6.0%
0 2583
 
5.7%
9 2208
 
4.9%
Latin
ValueCountFrequency (%)
N 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11013
24.5%
2 9391
20.9%
3 4455
9.9%
4 3750
 
8.3%
6 3040
 
6.8%
5 2910
 
6.5%
7 2859
 
6.4%
8 2715
 
6.0%
0 2583
 
5.7%
9 2208
 
4.9%
Other values (2) 12
 
< 0.1%

grid
Real number (ℝ)

Zeros 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.134796
Minimum0
Maximum34
Zeros1638
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:20.049209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median11
Q317
95-th percentile23
Maximum34
Range34
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2028596
Coefficient of variation (CV)0.64687847
Kurtosis-0.92349181
Mean11.134796
Median Absolute Deviation (MAD)6
Skewness0.19427022
Sum297956
Variance51.881187
MonotonicityNot monotonic
2025-08-20T23:50:20.101959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 1638
 
6.1%
1 1136
 
4.2%
7 1135
 
4.2%
5 1132
 
4.2%
11 1132
 
4.2%
9 1132
 
4.2%
4 1132
 
4.2%
10 1130
 
4.2%
3 1130
 
4.2%
8 1129
 
4.2%
Other values (25) 14933
55.8%
ValueCountFrequency (%)
0 1638
6.1%
1 1136
4.2%
2 1125
4.2%
3 1130
4.2%
4 1132
4.2%
5 1132
4.2%
6 1125
4.2%
7 1135
4.2%
8 1129
4.2%
9 1132
4.2%
ValueCountFrequency (%)
34 1
 
< 0.1%
33 13
 
< 0.1%
32 17
 
0.1%
31 18
 
0.1%
30 19
 
0.1%
29 25
 
0.1%
28 30
 
0.1%
27 46
 
0.2%
26 248
0.9%
25 301
1.1%

position
Categorical

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
\N
10953 
3
1135 
4
1135 
2
1133 
5
1131 
Other values (29)
11272 

Length

Max length2
Median length2
Mean length1.6261445
Min length1

Characters and Unicode

Total characters43514
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
\N 10953
40.9%
3 1135
 
4.2%
4 1135
 
4.2%
2 1133
 
4.2%
5 1131
 
4.2%
1 1128
 
4.2%
6 1124
 
4.2%
7 1104
 
4.1%
8 1076
 
4.0%
9 1038
 
3.9%
Other values (24) 5802
21.7%

Length

2025-08-20T23:50:20.158048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 10953
40.9%
3 1135
 
4.2%
4 1135
 
4.2%
2 1133
 
4.2%
5 1131
 
4.2%
1 1128
 
4.2%
6 1124
 
4.2%
7 1104
 
4.1%
8 1076
 
4.0%
9 1038
 
3.9%
Other values (24) 5802
21.7%

Most occurring characters

ValueCountFrequency (%)
\ 10953
25.2%
N 10953
25.2%
1 7721
17.7%
2 2094
 
4.8%
3 1861
 
4.3%
4 1743
 
4.0%
5 1660
 
3.8%
6 1557
 
3.6%
7 1441
 
3.3%
8 1300
 
3.0%
Other values (2) 2231
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21608
49.7%
Other Punctuation 10953
25.2%
Uppercase Letter 10953
25.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7721
35.7%
2 2094
 
9.7%
3 1861
 
8.6%
4 1743
 
8.1%
5 1660
 
7.7%
6 1557
 
7.2%
7 1441
 
6.7%
8 1300
 
6.0%
9 1180
 
5.5%
0 1051
 
4.9%
Other Punctuation
ValueCountFrequency (%)
\ 10953
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 10953
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32561
74.8%
Latin 10953
 
25.2%

Most frequent character per script

Common
ValueCountFrequency (%)
\ 10953
33.6%
1 7721
23.7%
2 2094
 
6.4%
3 1861
 
5.7%
4 1743
 
5.4%
5 1660
 
5.1%
6 1557
 
4.8%
7 1441
 
4.4%
8 1300
 
4.0%
9 1180
 
3.6%
Latin
ValueCountFrequency (%)
N 10953
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
\ 10953
25.2%
N 10953
25.2%
1 7721
17.7%
2 2094
 
4.8%
3 1861
 
4.3%
4 1743
 
4.0%
5 1660
 
3.8%
6 1557
 
3.6%
7 1441
 
3.3%
8 1300
 
3.0%
Other values (2) 2231
 
5.1%

positionText
Categorical

High correlation 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
R
8897 
F
1368 
3
 
1135
4
 
1135
2
 
1133
Other values (34)
13091 

Length

Max length2
Median length1
Mean length1.216899
Min length1

Characters and Unicode

Total characters32563
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
R 8897
33.2%
F 1368
 
5.1%
3 1135
 
4.2%
4 1135
 
4.2%
2 1133
 
4.2%
5 1131
 
4.2%
1 1128
 
4.2%
6 1124
 
4.2%
7 1104
 
4.1%
8 1076
 
4.0%
Other values (29) 7528
28.1%

Length

2025-08-20T23:50:20.205411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 8897
33.2%
f 1368
 
5.1%
3 1135
 
4.2%
4 1135
 
4.2%
2 1133
 
4.2%
5 1131
 
4.2%
1 1128
 
4.2%
6 1124
 
4.2%
7 1104
 
4.1%
8 1076
 
4.0%
Other values (29) 7528
28.1%

Most occurring characters

ValueCountFrequency (%)
R 8897
27.3%
1 7724
23.7%
2 2094
 
6.4%
3 1861
 
5.7%
4 1744
 
5.4%
5 1660
 
5.1%
6 1557
 
4.8%
7 1441
 
4.4%
F 1368
 
4.2%
8 1300
 
4.0%
Other values (6) 2917
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21612
66.4%
Uppercase Letter 10951
33.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7724
35.7%
2 2094
 
9.7%
3 1861
 
8.6%
4 1744
 
8.1%
5 1660
 
7.7%
6 1557
 
7.2%
7 1441
 
6.7%
8 1300
 
6.0%
9 1180
 
5.5%
0 1051
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
R 8897
81.2%
F 1368
 
12.5%
W 336
 
3.1%
N 190
 
1.7%
D 151
 
1.4%
E 9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21612
66.4%
Latin 10951
33.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7724
35.7%
2 2094
 
9.7%
3 1861
 
8.6%
4 1744
 
8.1%
5 1660
 
7.7%
6 1557
 
7.2%
7 1441
 
6.7%
8 1300
 
6.0%
9 1180
 
5.5%
0 1051
 
4.9%
Latin
ValueCountFrequency (%)
R 8897
81.2%
F 1368
 
12.5%
W 336
 
3.1%
N 190
 
1.7%
D 151
 
1.4%
E 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 8897
27.3%
1 7724
23.7%
2 2094
 
6.4%
3 1861
 
5.7%
4 1744
 
5.4%
5 1660
 
5.1%
6 1557
 
4.8%
7 1441
 
4.4%
F 1368
 
4.2%
8 1300
 
4.0%
Other values (6) 2917
 
9.0%

positionOrder
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.794051
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:20.254311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q318
95-th percentile26
Maximum39
Range38
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.6659506
Coefficient of variation (CV)0.59918089
Kurtosis-0.47435927
Mean12.794051
Median Absolute Deviation (MAD)6
Skewness0.39938197
Sum342356
Variance58.766799
MonotonicityNot monotonic
2025-08-20T23:50:20.314209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3 1135
 
4.2%
4 1135
 
4.2%
2 1134
 
4.2%
11 1133
 
4.2%
5 1132
 
4.2%
6 1132
 
4.2%
7 1132
 
4.2%
8 1132
 
4.2%
9 1131
 
4.2%
10 1130
 
4.2%
Other values (29) 15433
57.7%
ValueCountFrequency (%)
1 1128
4.2%
2 1134
4.2%
3 1135
4.2%
4 1135
4.2%
5 1132
4.2%
6 1132
4.2%
7 1132
4.2%
8 1132
4.2%
9 1131
4.2%
10 1130
4.2%
ValueCountFrequency (%)
39 13
 
< 0.1%
38 17
 
0.1%
37 17
 
0.1%
36 18
 
0.1%
35 29
 
0.1%
34 46
 
0.2%
33 65
0.2%
32 79
0.3%
31 117
0.4%
30 156
0.6%

points
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9876322
Minimum0
Maximum50
Zeros18589
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:20.370071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.3512089
Coefficient of variation (CV)2.1891419
Kurtosis10.733137
Mean1.9876322
Median Absolute Deviation (MAD)0
Skewness3.0441067
Sum53187.05
Variance18.933019
MonotonicityNot monotonic
2025-08-20T23:50:20.568772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 18589
69.5%
2 1127
 
4.2%
4 1111
 
4.2%
6 1090
 
4.1%
1 1066
 
4.0%
3 823
 
3.1%
10 613
 
2.3%
8 474
 
1.8%
9 444
 
1.7%
12 293
 
1.1%
Other values (29) 1129
 
4.2%
ValueCountFrequency (%)
0 18589
69.5%
0.5 6
 
< 0.1%
1 1066
 
4.0%
1.33 3
 
< 0.1%
1.5 17
 
0.1%
2 1127
 
4.2%
2.5 1
 
< 0.1%
3 823
 
3.1%
3.14 1
 
< 0.1%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
36 1
 
< 0.1%
30 1
 
< 0.1%
26 37
 
0.1%
25 266
1.0%
24 1
 
< 0.1%
20 1
 
< 0.1%
19 23
 
0.1%
18 280
1.0%
16 13
 
< 0.1%

laps
Real number (ℝ)

High correlation  Zeros 

Distinct172
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.301768
Minimum0
Maximum200
Zeros2532
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:20.630687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median53
Q366
95-th percentile79
Maximum200
Range200
Interquartile range (IQR)43

Descriptive statistics

Standard deviation29.496557
Coefficient of variation (CV)0.63705034
Kurtosis3.6908804
Mean46.301768
Median Absolute Deviation (MAD)17
Skewness0.69643443
Sum1238989
Variance870.04687
MonotonicityNot monotonic
2025-08-20T23:50:20.693110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2532
 
9.5%
70 1000
 
3.7%
53 937
 
3.5%
52 821
 
3.1%
56 809
 
3.0%
57 715
 
2.7%
69 688
 
2.6%
71 658
 
2.5%
55 589
 
2.2%
58 567
 
2.1%
Other values (162) 17443
65.2%
ValueCountFrequency (%)
0 2532
9.5%
1 308
 
1.2%
2 228
 
0.9%
3 199
 
0.7%
4 183
 
0.7%
5 197
 
0.7%
6 183
 
0.7%
7 167
 
0.6%
8 188
 
0.7%
9 178
 
0.7%
ValueCountFrequency (%)
200 123
0.5%
199 4
 
< 0.1%
197 5
 
< 0.1%
196 15
 
0.1%
195 4
 
< 0.1%
194 4
 
< 0.1%
193 7
 
< 0.1%
192 1
 
< 0.1%
191 8
 
< 0.1%
190 2
 
< 0.1%

time_x
Text

Distinct7411
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-20T23:50:20.807133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.6445308
Min length2

Characters and Unicode

Total characters97524
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7167 ?
Unique (%)26.8%

Sample

1st row2:13:23.6
2nd row+2.6
3rd row+52.0
4th row\N
5th row\N
ValueCountFrequency (%)
n 19079
71.3%
8:22.19 5
 
< 0.1%
5.7 4
 
< 0.1%
1:29.6 4
 
< 0.1%
0.7 4
 
< 0.1%
46.2 4
 
< 0.1%
31.8 3
 
< 0.1%
20.2 3
 
< 0.1%
52.0 3
 
< 0.1%
14.1 3
 
< 0.1%
Other values (7401) 7647
28.6%
2025-08-20T23:50:20.979461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 19079
19.6%
N 19079
19.6%
. 7679
7.9%
1 7447
 
7.6%
+ 6552
 
6.7%
2 4763
 
4.9%
3 4583
 
4.7%
: 4446
 
4.6%
4 4210
 
4.3%
5 4006
 
4.1%
Other values (5) 15680
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40689
41.7%
Other Punctuation 31204
32.0%
Uppercase Letter 19079
19.6%
Math Symbol 6552
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7447
18.3%
2 4763
11.7%
3 4583
11.3%
4 4210
10.3%
5 4006
9.8%
0 3858
9.5%
6 3004
7.4%
9 2956
 
7.3%
7 2935
 
7.2%
8 2927
 
7.2%
Other Punctuation
ValueCountFrequency (%)
\ 19079
61.1%
. 7679
24.6%
: 4446
 
14.2%
Uppercase Letter
ValueCountFrequency (%)
N 19079
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78445
80.4%
Latin 19079
 
19.6%

Most frequent character per script

Common
ValueCountFrequency (%)
\ 19079
24.3%
. 7679
9.8%
1 7447
 
9.5%
+ 6552
 
8.4%
2 4763
 
6.1%
3 4583
 
5.8%
: 4446
 
5.7%
4 4210
 
5.4%
5 4006
 
5.1%
0 3858
 
4.9%
Other values (4) 11822
15.1%
Latin
ValueCountFrequency (%)
N 19079
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
\ 19079
19.6%
N 19079
19.6%
. 7679
7.9%
1 7447
 
7.6%
+ 6552
 
6.7%
2 4763
 
4.9%
3 4583
 
4.7%
: 4446
 
4.6%
4 4210
 
4.3%
5 4006
 
4.1%
Other values (5) 15680
16.1%
Distinct7639
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-20T23:50:21.087259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length3.4447476
Min length2

Characters and Unicode

Total characters92178
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7600 ?
Unique (%)28.4%

Sample

1st row8003600
2nd row8006200
3rd row8055600
4th row\N
5th row\N
ValueCountFrequency (%)
n 19079
71.3%
14259460 5
 
< 0.1%
10928200 3
 
< 0.1%
5942789 2
 
< 0.1%
5350182 2
 
< 0.1%
5808819 2
 
< 0.1%
8627000 2
 
< 0.1%
5262136 2
 
< 0.1%
5342067 2
 
< 0.1%
11197800 2
 
< 0.1%
Other values (7629) 7658
28.6%
2025-08-20T23:50:21.240723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 19079
20.7%
N 19079
20.7%
5 8471
9.2%
0 6883
 
7.5%
6 6051
 
6.6%
4 5063
 
5.5%
7 4891
 
5.3%
1 4654
 
5.0%
2 4583
 
5.0%
8 4562
 
4.9%
Other values (2) 8862
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54020
58.6%
Other Punctuation 19079
 
20.7%
Uppercase Letter 19079
 
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 8471
15.7%
0 6883
12.7%
6 6051
11.2%
4 5063
9.4%
7 4891
9.1%
1 4654
8.6%
2 4583
8.5%
8 4562
8.4%
3 4461
8.3%
9 4401
8.1%
Other Punctuation
ValueCountFrequency (%)
\ 19079
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 19079
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73099
79.3%
Latin 19079
 
20.7%

Most frequent character per script

Common
ValueCountFrequency (%)
\ 19079
26.1%
5 8471
11.6%
0 6883
 
9.4%
6 6051
 
8.3%
4 5063
 
6.9%
7 4891
 
6.7%
1 4654
 
6.4%
2 4583
 
6.3%
8 4562
 
6.2%
3 4461
 
6.1%
Latin
ValueCountFrequency (%)
N 19079
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
\ 19079
20.7%
N 19079
20.7%
5 8471
9.2%
0 6883
 
7.5%
6 6051
 
6.6%
4 5063
 
5.5%
7 4891
 
5.3%
1 4654
 
5.0%
2 4583
 
5.0%
8 4562
 
4.9%
Other values (2) 8862
9.6%
Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-20T23:50:21.329777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9855749
Min length1

Characters and Unicode

Total characters53132
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N
ValueCountFrequency (%)
n 18507
69.2%
50 309
 
1.2%
52 289
 
1.1%
53 287
 
1.1%
51 275
 
1.0%
48 230
 
0.9%
44 224
 
0.8%
55 220
 
0.8%
49 219
 
0.8%
43 217
 
0.8%
Other values (71) 5982
 
22.4%
2025-08-20T23:50:21.458742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 18507
34.8%
N 18507
34.8%
5 2988
 
5.6%
4 2917
 
5.5%
3 2035
 
3.8%
6 1748
 
3.3%
2 1580
 
3.0%
1 1436
 
2.7%
7 1016
 
1.9%
0 818
 
1.5%
Other values (2) 1580
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 18507
34.8%
Uppercase Letter 18507
34.8%
Decimal Number 16118
30.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2988
18.5%
4 2917
18.1%
3 2035
12.6%
6 1748
10.8%
2 1580
9.8%
1 1436
8.9%
7 1016
 
6.3%
0 818
 
5.1%
9 800
 
5.0%
8 780
 
4.8%
Other Punctuation
ValueCountFrequency (%)
\ 18507
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 18507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34625
65.2%
Latin 18507
34.8%

Most frequent character per script

Common
ValueCountFrequency (%)
\ 18507
53.4%
5 2988
 
8.6%
4 2917
 
8.4%
3 2035
 
5.9%
6 1748
 
5.0%
2 1580
 
4.6%
1 1436
 
4.1%
7 1016
 
2.9%
0 818
 
2.4%
9 800
 
2.3%
Latin
ValueCountFrequency (%)
N 18507
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
\ 18507
34.8%
N 18507
34.8%
5 2988
 
5.6%
4 2917
 
5.5%
3 2035
 
3.8%
6 1748
 
3.3%
2 1580
 
3.0%
1 1436
 
2.7%
7 1016
 
1.9%
0 818
 
1.5%
Other values (2) 1580
 
3.0%

rank
Categorical

Imbalance 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
\N
18249 
2
 
410
3
 
410
5
 
410
4
 
410
Other values (21)
6870 

Length

Max length2
Median length2
Mean length1.852573
Min length1

Characters and Unicode

Total characters49573
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N

Common Values

ValueCountFrequency (%)
\N 18249
68.2%
2 410
 
1.5%
3 410
 
1.5%
5 410
 
1.5%
4 410
 
1.5%
6 410
 
1.5%
1 410
 
1.5%
7 409
 
1.5%
9 409
 
1.5%
11 409
 
1.5%
Other values (16) 4823
 
18.0%

Length

2025-08-20T23:50:21.519778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 18249
68.2%
2 410
 
1.5%
3 410
 
1.5%
5 410
 
1.5%
4 410
 
1.5%
6 410
 
1.5%
1 410
 
1.5%
7 409
 
1.5%
9 409
 
1.5%
11 409
 
1.5%
Other values (16) 4823
 
18.0%

Most occurring characters

ValueCountFrequency (%)
\ 18249
36.8%
N 18249
36.8%
1 4934
 
10.0%
2 1481
 
3.0%
0 955
 
1.9%
3 861
 
1.7%
4 846
 
1.7%
5 817
 
1.6%
6 816
 
1.6%
7 809
 
1.6%
Other values (2) 1556
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 18249
36.8%
Uppercase Letter 18249
36.8%
Decimal Number 13075
26.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4934
37.7%
2 1481
 
11.3%
0 955
 
7.3%
3 861
 
6.6%
4 846
 
6.5%
5 817
 
6.2%
6 816
 
6.2%
7 809
 
6.2%
8 799
 
6.1%
9 757
 
5.8%
Other Punctuation
ValueCountFrequency (%)
\ 18249
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 18249
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31324
63.2%
Latin 18249
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
\ 18249
58.3%
1 4934
 
15.8%
2 1481
 
4.7%
0 955
 
3.0%
3 861
 
2.7%
4 846
 
2.7%
5 817
 
2.6%
6 816
 
2.6%
7 809
 
2.6%
8 799
 
2.6%
Latin
ValueCountFrequency (%)
N 18249
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
\ 18249
36.8%
N 18249
36.8%
1 4934
 
10.0%
2 1481
 
3.0%
0 955
 
1.9%
3 861
 
1.7%
4 846
 
1.7%
5 817
 
1.6%
6 816
 
1.6%
7 809
 
1.6%
Other values (2) 1556
 
3.1%
Distinct7474
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-20T23:50:21.618711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length3.8502934
Min length2

Characters and Unicode

Total characters103030
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6759 ?
Unique (%)25.3%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N
ValueCountFrequency (%)
n 18507
69.2%
1:17.495 4
 
< 0.1%
1:43.026 4
 
< 0.1%
1:18.262 4
 
< 0.1%
1:14.117 4
 
< 0.1%
1:18.904 4
 
< 0.1%
1:21.134 3
 
< 0.1%
1:23.488 3
 
< 0.1%
1:37.036 3
 
< 0.1%
1:19.820 3
 
< 0.1%
Other values (7464) 8220
30.7%
2025-08-20T23:50:21.788951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 18507
18.0%
N 18507
18.0%
1 13080
12.7%
: 8252
8.0%
. 8252
8.0%
2 5518
 
5.4%
3 5450
 
5.3%
4 4681
 
4.5%
5 3759
 
3.6%
0 3523
 
3.4%
Other values (4) 13501
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49512
48.1%
Other Punctuation 35011
34.0%
Uppercase Letter 18507
 
18.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13080
26.4%
2 5518
11.1%
3 5450
11.0%
4 4681
 
9.5%
5 3759
 
7.6%
0 3523
 
7.1%
7 3432
 
6.9%
6 3384
 
6.8%
8 3365
 
6.8%
9 3320
 
6.7%
Other Punctuation
ValueCountFrequency (%)
\ 18507
52.9%
: 8252
23.6%
. 8252
23.6%
Uppercase Letter
ValueCountFrequency (%)
N 18507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84523
82.0%
Latin 18507
 
18.0%

Most frequent character per script

Common
ValueCountFrequency (%)
\ 18507
21.9%
1 13080
15.5%
: 8252
9.8%
. 8252
9.8%
2 5518
 
6.5%
3 5450
 
6.4%
4 4681
 
5.5%
5 3759
 
4.4%
0 3523
 
4.2%
7 3432
 
4.1%
Other values (3) 10069
11.9%
Latin
ValueCountFrequency (%)
N 18507
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
\ 18507
18.0%
N 18507
18.0%
1 13080
12.7%
: 8252
8.0%
. 8252
8.0%
2 5518
 
5.4%
3 5450
 
5.3%
4 4681
 
4.5%
5 3759
 
3.6%
0 3523
 
3.4%
Other values (4) 13501
13.1%
Distinct7725
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-20T23:50:21.911868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length3.5418364
Min length2

Characters and Unicode

Total characters94776
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7225 ?
Unique (%)27.0%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N
ValueCountFrequency (%)
n 18507
69.2%
207.069 4
 
< 0.1%
200.642 3
 
< 0.1%
194.706 3
 
< 0.1%
220.611 3
 
< 0.1%
208.575 3
 
< 0.1%
222.592 3
 
< 0.1%
200.091 3
 
< 0.1%
207.249 3
 
< 0.1%
194.610 3
 
< 0.1%
Other values (7715) 8224
30.7%
2025-08-20T23:50:22.079786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 18507
19.5%
N 18507
19.5%
2 9324
9.8%
. 8252
8.7%
1 7848
8.3%
0 5231
 
5.5%
9 4751
 
5.0%
3 3987
 
4.2%
8 3962
 
4.2%
5 3737
 
3.9%
Other values (3) 10670
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49510
52.2%
Other Punctuation 26759
28.2%
Uppercase Letter 18507
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9324
18.8%
1 7848
15.9%
0 5231
10.6%
9 4751
9.6%
3 3987
8.1%
8 3962
8.0%
5 3737
7.5%
6 3619
 
7.3%
4 3610
 
7.3%
7 3441
 
7.0%
Other Punctuation
ValueCountFrequency (%)
\ 18507
69.2%
. 8252
30.8%
Uppercase Letter
ValueCountFrequency (%)
N 18507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76269
80.5%
Latin 18507
 
19.5%

Most frequent character per script

Common
ValueCountFrequency (%)
\ 18507
24.3%
2 9324
12.2%
. 8252
10.8%
1 7848
10.3%
0 5231
 
6.9%
9 4751
 
6.2%
3 3987
 
5.2%
8 3962
 
5.2%
5 3737
 
4.9%
6 3619
 
4.7%
Other values (2) 7051
 
9.2%
Latin
ValueCountFrequency (%)
N 18507
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
\ 18507
19.5%
N 18507
19.5%
2 9324
9.8%
. 8252
8.7%
1 7848
8.3%
0 5231
 
5.5%
9 4751
 
5.0%
3 3987
 
4.2%
8 3962
 
4.2%
5 3737
 
3.9%
Other values (3) 10670
11.3%

statusId
Real number (ℝ)

High correlation 

Distinct137
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.224971
Minimum1
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:22.148310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median10
Q314
95-th percentile81
Maximum141
Range140
Interquartile range (IQR)13

Descriptive statistics

Standard deviation26.026104
Coefficient of variation (CV)1.510952
Kurtosis4.1957953
Mean17.224971
Median Absolute Deviation (MAD)9
Skewness2.2384412
Sum460923
Variance677.35809
MonotonicityNot monotonic
2025-08-20T23:50:22.204108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7674
28.7%
11 4037
15.1%
5 2026
 
7.6%
12 1613
 
6.0%
3 1062
 
4.0%
81 1025
 
3.8%
4 854
 
3.2%
6 810
 
3.0%
20 795
 
3.0%
13 731
 
2.7%
Other values (127) 6132
22.9%
ValueCountFrequency (%)
1 7674
28.7%
2 147
 
0.5%
3 1062
 
4.0%
4 854
 
3.2%
5 2026
 
7.6%
6 810
 
3.0%
7 321
 
1.2%
8 214
 
0.8%
9 139
 
0.5%
10 316
 
1.2%
ValueCountFrequency (%)
141 1
 
< 0.1%
140 4
 
< 0.1%
139 3
 
< 0.1%
138 1
 
< 0.1%
137 2
 
< 0.1%
136 1
 
< 0.1%
135 1
 
< 0.1%
132 5
 
< 0.1%
131 42
0.2%
130 60
0.2%
Distinct861
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-08-20T23:50:22.278751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length7.4076759
Min length3

Characters and Unicode

Total characters198222
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)0.7%

Sample

1st rowfarina
2nd rowfagioli
3rd rowreg_parnell
4th rowcabantous
5th rowrosier
ValueCountFrequency (%)
alonso 404
 
1.5%
hamilton 356
 
1.3%
raikkonen 352
 
1.3%
barrichello 326
 
1.2%
button 309
 
1.2%
michael_schumacher 308
 
1.2%
vettel 300
 
1.1%
perez 283
 
1.1%
massa 271
 
1.0%
ricciardo 257
 
1.0%
Other values (851) 23593
88.2%
2025-08-20T23:50:22.420274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 21346
 
10.8%
a 19711
 
9.9%
r 16846
 
8.5%
i 14962
 
7.5%
n 14720
 
7.4%
l 14172
 
7.1%
o 12727
 
6.4%
s 11987
 
6.0%
t 10775
 
5.4%
c 6582
 
3.3%
Other values (20) 54394
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 195933
98.8%
Connector Punctuation 2266
 
1.1%
Dash Punctuation 20
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21346
10.9%
a 19711
 
10.1%
r 16846
 
8.6%
i 14962
 
7.6%
n 14720
 
7.5%
l 14172
 
7.2%
o 12727
 
6.5%
s 11987
 
6.1%
t 10775
 
5.5%
c 6582
 
3.4%
Other values (16) 52105
26.6%
Uppercase Letter
ValueCountFrequency (%)
C 2
66.7%
B 1
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 2266
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 195936
98.8%
Common 2286
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21346
10.9%
a 19711
 
10.1%
r 16846
 
8.6%
i 14962
 
7.6%
n 14720
 
7.5%
l 14172
 
7.2%
o 12727
 
6.5%
s 11987
 
6.1%
t 10775
 
5.5%
c 6582
 
3.4%
Other values (18) 52108
26.6%
Common
ValueCountFrequency (%)
_ 2266
99.1%
- 20
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 21346
 
10.8%
a 19711
 
9.9%
r 16846
 
8.5%
i 14962
 
7.5%
n 14720
 
7.4%
l 14172
 
7.1%
o 12727
 
6.4%
s 11987
 
6.0%
t 10775
 
5.4%
c 6582
 
3.3%
Other values (20) 54394
27.4%

driver_number
Categorical

High correlation  Imbalance 

Distinct49
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
\N
20198 
14
 
404
22
 
399
44
 
356
7
 
352
Other values (44)
5050 

Length

Max length2
Median length2
Mean length1.9358347
Min length1

Characters and Unicode

Total characters51801
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N

Common Values

ValueCountFrequency (%)
\N 20198
75.5%
14 404
 
1.5%
22 399
 
1.5%
44 356
 
1.3%
7 352
 
1.3%
5 300
 
1.1%
11 283
 
1.1%
19 271
 
1.0%
6 267
 
1.0%
3 257
 
1.0%
Other values (39) 3672
 
13.7%

Length

2025-08-20T23:50:22.484216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 20198
75.5%
14 404
 
1.5%
22 399
 
1.5%
44 356
 
1.3%
7 352
 
1.3%
5 300
 
1.1%
11 283
 
1.1%
19 271
 
1.0%
6 267
 
1.0%
3 257
 
1.0%
Other values (39) 3672
 
13.7%

Most occurring characters

ValueCountFrequency (%)
\ 20198
39.0%
N 20198
39.0%
1 2233
 
4.3%
2 1789
 
3.5%
4 1499
 
2.9%
3 1246
 
2.4%
7 1154
 
2.2%
9 823
 
1.6%
5 803
 
1.6%
8 678
 
1.3%
Other values (2) 1180
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 20198
39.0%
Uppercase Letter 20198
39.0%
Decimal Number 11405
22.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2233
19.6%
2 1789
15.7%
4 1499
13.1%
3 1246
10.9%
7 1154
10.1%
9 823
 
7.2%
5 803
 
7.0%
8 678
 
5.9%
6 657
 
5.8%
0 523
 
4.6%
Other Punctuation
ValueCountFrequency (%)
\ 20198
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 20198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31603
61.0%
Latin 20198
39.0%

Most frequent character per script

Common
ValueCountFrequency (%)
\ 20198
63.9%
1 2233
 
7.1%
2 1789
 
5.7%
4 1499
 
4.7%
3 1246
 
3.9%
7 1154
 
3.7%
9 823
 
2.6%
5 803
 
2.5%
8 678
 
2.1%
6 657
 
2.1%
Latin
ValueCountFrequency (%)
N 20198
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
\ 20198
39.0%
N 20198
39.0%
1 2233
 
4.3%
2 1789
 
3.5%
4 1499
 
2.9%
3 1246
 
2.4%
7 1154
 
2.2%
9 823
 
1.6%
5 803
 
1.6%
8 678
 
1.3%
Other values (2) 1180
 
2.3%

code
Text

Distinct98
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-08-20T23:50:22.560779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.380358
Min length2

Characters and Unicode

Total characters63696
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N
ValueCountFrequency (%)
n 16581
62.0%
alo 404
 
1.5%
ham 356
 
1.3%
msc 352
 
1.3%
rai 352
 
1.3%
bar 326
 
1.2%
but 309
 
1.2%
vet 300
 
1.1%
per 283
 
1.1%
mas 271
 
1.0%
Other values (88) 7225
27.0%
2025-08-20T23:50:22.700497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 17007
26.7%
\ 16581
26.0%
A 3273
 
5.1%
R 3055
 
4.8%
S 2407
 
3.8%
O 2376
 
3.7%
I 2027
 
3.2%
T 1827
 
2.9%
U 1778
 
2.8%
E 1773
 
2.8%
Other values (15) 11592
18.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 47115
74.0%
Other Punctuation 16581
 
26.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 17007
36.1%
A 3273
 
6.9%
R 3055
 
6.5%
S 2407
 
5.1%
O 2376
 
5.0%
I 2027
 
4.3%
T 1827
 
3.9%
U 1778
 
3.8%
E 1773
 
3.8%
L 1694
 
3.6%
Other values (14) 9898
21.0%
Other Punctuation
ValueCountFrequency (%)
\ 16581
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47115
74.0%
Common 16581
 
26.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 17007
36.1%
A 3273
 
6.9%
R 3055
 
6.5%
S 2407
 
5.1%
O 2376
 
5.0%
I 2027
 
4.3%
T 1827
 
3.9%
U 1778
 
3.8%
E 1773
 
3.8%
L 1694
 
3.6%
Other values (14) 9898
21.0%
Common
ValueCountFrequency (%)
\ 16581
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 17007
26.7%
\ 16581
26.0%
A 3273
 
5.1%
R 3055
 
4.8%
S 2407
 
3.8%
O 2376
 
3.7%
I 2027
 
3.2%
T 1827
 
2.9%
U 1778
 
2.8%
E 1773
 
2.8%
Other values (15) 11592
18.2%
Distinct478
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-08-20T23:50:22.806390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length5.8056729
Min length2

Characters and Unicode

Total characters155354
Distinct characters65
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.2%

Sample

1st rowNino
2nd rowLuigi
3rd rowReg
4th rowYves
5th rowLouis
ValueCountFrequency (%)
carlos 439
 
1.6%
nico 436
 
1.6%
fernando 404
 
1.5%
jacques 360
 
1.3%
lewis 356
 
1.3%
kimi 352
 
1.3%
michael 334
 
1.2%
rubens 326
 
1.2%
david 318
 
1.2%
john 311
 
1.2%
Other values (472) 23199
86.5%
2025-08-20T23:50:22.971854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 15546
 
10.0%
i 15265
 
9.8%
a 15067
 
9.7%
n 12513
 
8.1%
r 12230
 
7.9%
o 10406
 
6.7%
l 6660
 
4.3%
c 5172
 
3.3%
s 5079
 
3.3%
t 4174
 
2.7%
Other values (55) 53242
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127198
81.9%
Uppercase Letter 27455
 
17.7%
Dash Punctuation 625
 
0.4%
Space Separator 76
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15546
12.2%
i 15265
12.0%
a 15067
11.8%
n 12513
9.8%
r 12230
9.6%
o 10406
8.2%
l 6660
 
5.2%
c 5172
 
4.1%
s 5079
 
4.0%
t 4174
 
3.3%
Other values (26) 25086
19.7%
Uppercase Letter
ValueCountFrequency (%)
J 4121
15.0%
M 2516
 
9.2%
P 2028
 
7.4%
R 2021
 
7.4%
A 1800
 
6.6%
N 1519
 
5.5%
D 1432
 
5.2%
G 1402
 
5.1%
C 1316
 
4.8%
S 1267
 
4.6%
Other values (17) 8033
29.3%
Dash Punctuation
ValueCountFrequency (%)
- 625
100.0%
Space Separator
ValueCountFrequency (%)
76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 154653
99.5%
Common 701
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15546
 
10.1%
i 15265
 
9.9%
a 15067
 
9.7%
n 12513
 
8.1%
r 12230
 
7.9%
o 10406
 
6.7%
l 6660
 
4.3%
c 5172
 
3.3%
s 5079
 
3.3%
t 4174
 
2.7%
Other values (53) 52541
34.0%
Common
ValueCountFrequency (%)
- 625
89.2%
76
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 154531
99.5%
None 823
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15546
 
10.1%
i 15265
 
9.9%
a 15067
 
9.8%
n 12513
 
8.1%
r 12230
 
7.9%
o 10406
 
6.7%
l 6660
 
4.3%
c 5172
 
3.3%
s 5079
 
3.3%
t 4174
 
2.7%
Other values (42) 52419
33.9%
None
ValueCountFrequency (%)
é 342
41.6%
É 175
21.3%
ç 84
 
10.2%
í 80
 
9.7%
á 58
 
7.0%
ô 40
 
4.9%
ó 21
 
2.6%
ú 6
 
0.7%
è 5
 
0.6%
ü 4
 
0.5%
Other values (3) 8
 
1.0%
Distinct802
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-08-20T23:50:23.092975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length16
Mean length7.061512
Min length3

Characters and Unicode

Total characters188959
Distinct characters66
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)0.6%

Sample

1st rowFarina
2nd rowFagioli
3rd rowParnell
4th rowCabantous
5th rowRosier
ValueCountFrequency (%)
de 601
 
2.2%
schumacher 532
 
1.9%
alonso 404
 
1.5%
hamilton 361
 
1.3%
hill 353
 
1.3%
räikkönen 352
 
1.3%
rosberg 334
 
1.2%
barrichello 326
 
1.2%
verstappen 316
 
1.1%
button 309
 
1.1%
Other values (799) 23958
86.0%
2025-08-20T23:50:23.276070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 20037
 
10.6%
a 16239
 
8.6%
r 14177
 
7.5%
i 13824
 
7.3%
n 13583
 
7.2%
l 12420
 
6.6%
o 11530
 
6.1%
t 9820
 
5.2%
s 8690
 
4.6%
u 5735
 
3.0%
Other values (56) 62904
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 160598
85.0%
Uppercase Letter 27134
 
14.4%
Space Separator 1087
 
0.6%
Dash Punctuation 80
 
< 0.1%
Other Punctuation 60
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20037
12.5%
a 16239
10.1%
r 14177
8.8%
i 13824
8.6%
n 13583
 
8.5%
l 12420
 
7.7%
o 11530
 
7.2%
t 9820
 
6.1%
s 8690
 
5.4%
u 5735
 
3.6%
Other values (26) 34543
21.5%
Uppercase Letter
ValueCountFrequency (%)
B 2969
10.9%
S 2947
10.9%
P 2161
 
8.0%
M 2094
 
7.7%
R 2000
 
7.4%
H 1983
 
7.3%
A 1898
 
7.0%
G 1573
 
5.8%
C 1338
 
4.9%
L 1169
 
4.3%
Other values (16) 7002
25.8%
Other Punctuation
ValueCountFrequency (%)
. 31
51.7%
' 29
48.3%
Space Separator
ValueCountFrequency (%)
1087
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 187732
99.4%
Common 1227
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20037
 
10.7%
a 16239
 
8.7%
r 14177
 
7.6%
i 13824
 
7.4%
n 13583
 
7.2%
l 12420
 
6.6%
o 11530
 
6.1%
t 9820
 
5.2%
s 8690
 
4.6%
u 5735
 
3.1%
Other values (52) 61677
32.9%
Common
ValueCountFrequency (%)
1087
88.6%
- 80
 
6.5%
. 31
 
2.5%
' 29
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187216
99.1%
None 1743
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20037
 
10.7%
a 16239
 
8.7%
r 14177
 
7.6%
i 13824
 
7.4%
n 13583
 
7.3%
l 12420
 
6.6%
o 11530
 
6.2%
t 9820
 
5.2%
s 8690
 
4.6%
u 5735
 
3.1%
Other values (45) 61161
32.7%
None
ValueCountFrequency (%)
ä 587
33.7%
é 421
24.2%
ö 352
20.2%
ü 230
 
13.2%
í 60
 
3.4%
á 32
 
1.8%
è 32
 
1.8%
ó 12
 
0.7%
É 12
 
0.7%
ø 4
 
0.2%

dob
Date

Distinct843
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
Minimum1896-12-28 00:00:00
Maximum2005-05-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-20T23:50:23.345712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:23.404497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

driver_nationality
Categorical

High correlation 

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
British
4559 
Italian
3418 
French
3095 
German
2430 
Brazilian
1953 
Other values (38)
11304 

Length

Max length17
Median length16
Mean length7.2786726
Min length4

Characters and Unicode

Total characters194770
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowItalian
2nd rowItalian
3rd rowBritish
4th rowFrench
5th rowFrench

Common Values

ValueCountFrequency (%)
British 4559
17.0%
Italian 3418
12.8%
French 3095
11.6%
German 2430
 
9.1%
Brazilian 1953
 
7.3%
American 1315
 
4.9%
Finnish 1193
 
4.5%
Spanish 913
 
3.4%
Australian 893
 
3.3%
Austrian 690
 
2.6%
Other values (33) 6300
23.5%

Length

2025-08-20T23:50:23.466019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
british 4559
16.7%
italian 3418
12.5%
french 3095
11.3%
german 2435
 
8.9%
brazilian 1953
 
7.1%
american 1315
 
4.8%
finnish 1193
 
4.4%
spanish 913
 
3.3%
australian 893
 
3.3%
austrian 690
 
2.5%
Other values (35) 6917
25.3%

Most occurring characters

ValueCountFrequency (%)
i 26941
13.8%
a 24053
12.3%
n 22577
11.6%
r 16162
 
8.3%
e 12906
 
6.6%
h 11691
 
6.0%
s 11666
 
6.0%
t 10812
 
5.6%
l 7663
 
3.9%
B 7103
 
3.6%
Other values (35) 43196
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 166750
85.6%
Uppercase Letter 27385
 
14.1%
Space Separator 631
 
0.3%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 26941
16.2%
a 24053
14.4%
n 22577
13.5%
r 16162
9.7%
e 12906
7.7%
h 11691
7.0%
s 11666
7.0%
t 10812
6.5%
l 7663
 
4.6%
c 5642
 
3.4%
Other values (13) 16637
10.0%
Uppercase Letter
ValueCountFrequency (%)
B 7103
25.9%
F 4288
15.7%
I 3585
13.1%
A 3494
12.8%
G 2435
 
8.9%
S 2129
 
7.8%
D 757
 
2.8%
C 731
 
2.7%
J 681
 
2.5%
M 662
 
2.4%
Other values (10) 1520
 
5.6%
Space Separator
ValueCountFrequency (%)
631
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194135
99.7%
Common 635
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 26941
13.9%
a 24053
12.4%
n 22577
11.6%
r 16162
 
8.3%
e 12906
 
6.6%
h 11691
 
6.0%
s 11666
 
6.0%
t 10812
 
5.6%
l 7663
 
3.9%
B 7103
 
3.7%
Other values (33) 42561
21.9%
Common
ValueCountFrequency (%)
631
99.4%
- 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 26941
13.8%
a 24053
12.3%
n 22577
11.6%
r 16162
 
8.3%
e 12906
 
6.6%
h 11691
 
6.0%
s 11666
 
6.0%
t 10812
 
5.6%
l 7663
 
3.9%
B 7103
 
3.6%
Other values (35) 43196
22.2%
Distinct211
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-08-20T23:50:23.553883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length7.3007586
Min length2

Characters and Unicode

Total characters195361
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)0.1%

Sample

1st rowalfa
2nd rowalfa
3rd rowalfa
4th rowlago
5th rowlago
ValueCountFrequency (%)
ferrari 2439
 
9.1%
mclaren 1923
 
7.2%
williams 1676
 
6.3%
tyrrell 881
 
3.3%
team_lotus 871
 
3.3%
sauber 837
 
3.1%
red_bull 788
 
2.9%
renault 787
 
2.9%
minardi 672
 
2.5%
brabham 662
 
2.5%
Other values (201) 15223
56.9%
2025-08-20T23:50:23.711215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 27334
14.0%
a 23547
12.1%
e 17297
 
8.9%
l 15726
 
8.0%
i 13254
 
6.8%
o 11760
 
6.0%
s 11181
 
5.7%
m 10910
 
5.6%
t 10285
 
5.3%
n 7828
 
4.0%
Other values (19) 46239
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 189909
97.2%
Connector Punctuation 3721
 
1.9%
Dash Punctuation 1541
 
0.8%
Decimal Number 190
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 27334
14.4%
a 23547
12.4%
e 17297
9.1%
l 15726
 
8.3%
i 13254
 
7.0%
o 11760
 
6.2%
s 11181
 
5.9%
m 10910
 
5.7%
t 10285
 
5.4%
n 7828
 
4.1%
Other values (16) 40787
21.5%
Connector Punctuation
ValueCountFrequency (%)
_ 3721
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1541
100.0%
Decimal Number
ValueCountFrequency (%)
1 190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 189909
97.2%
Common 5452
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 27334
14.4%
a 23547
12.4%
e 17297
9.1%
l 15726
 
8.3%
i 13254
 
7.0%
o 11760
 
6.2%
s 11181
 
5.9%
m 10910
 
5.7%
t 10285
 
5.4%
n 7828
 
4.1%
Other values (16) 40787
21.5%
Common
ValueCountFrequency (%)
_ 3721
68.3%
- 1541
28.3%
1 190
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 27334
14.0%
a 23547
12.1%
e 17297
 
8.9%
l 15726
 
8.0%
i 13254
 
6.8%
o 11760
 
6.0%
s 11181
 
5.7%
m 10910
 
5.6%
t 10285
 
5.3%
n 7828
 
4.0%
Other values (19) 46239
23.7%
Distinct211
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-08-20T23:50:23.824043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length7.6430734
Min length2

Characters and Unicode

Total characters204521
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)0.1%

Sample

1st rowAlfa Romeo
2nd rowAlfa Romeo
3rd rowAlfa Romeo
4th rowTalbot-Lago
5th rowTalbot-Lago
ValueCountFrequency (%)
ferrari 2439
 
7.5%
mclaren 1923
 
5.9%
williams 1676
 
5.2%
team 1479
 
4.6%
lotus 1101
 
3.4%
sauber 977
 
3.0%
tyrrell 881
 
2.7%
red 788
 
2.4%
bull 788
 
2.4%
renault 787
 
2.4%
Other values (217) 19508
60.3%
2025-08-20T23:50:23.991446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 23687
 
11.6%
a 21980
 
10.7%
e 18135
 
8.9%
i 12784
 
6.3%
o 12495
 
6.1%
l 10955
 
5.4%
s 9303
 
4.5%
n 7765
 
3.8%
t 7120
 
3.5%
M 6277
 
3.1%
Other values (43) 74020
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 156777
76.7%
Uppercase Letter 39729
 
19.4%
Space Separator 5588
 
2.7%
Dash Punctuation 1623
 
0.8%
Decimal Number 798
 
0.4%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 23687
15.1%
a 21980
14.0%
e 18135
11.6%
i 12784
8.2%
o 12495
8.0%
l 10955
7.0%
s 9303
 
5.9%
n 7765
 
5.0%
t 7120
 
4.5%
u 5850
 
3.7%
Other values (16) 26703
17.0%
Uppercase Letter
ValueCountFrequency (%)
M 6277
15.8%
L 4853
12.2%
F 4508
11.3%
R 4118
10.4%
T 3950
9.9%
B 3656
9.2%
A 2398
 
6.0%
S 2144
 
5.4%
W 2002
 
5.0%
C 1422
 
3.6%
Other values (12) 4401
11.1%
Other Punctuation
ValueCountFrequency (%)
& 3
50.0%
; 3
50.0%
Space Separator
ValueCountFrequency (%)
5588
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1623
100.0%
Decimal Number
ValueCountFrequency (%)
1 798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196506
96.1%
Common 8015
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 23687
 
12.1%
a 21980
 
11.2%
e 18135
 
9.2%
i 12784
 
6.5%
o 12495
 
6.4%
l 10955
 
5.6%
s 9303
 
4.7%
n 7765
 
4.0%
t 7120
 
3.6%
M 6277
 
3.2%
Other values (38) 66005
33.6%
Common
ValueCountFrequency (%)
5588
69.7%
- 1623
 
20.2%
1 798
 
10.0%
& 3
 
< 0.1%
; 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 23687
 
11.6%
a 21980
 
10.7%
e 18135
 
8.9%
i 12784
 
6.3%
o 12495
 
6.1%
l 10955
 
5.4%
s 9303
 
4.5%
n 7765
 
3.8%
t 7120
 
3.5%
M 6277
 
3.1%
Other values (43) 74020
36.2%
Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
British
12248 
Italian
5778 
French
2415 
Swiss
1296 
German
 
1091
Other values (19)
3931 

Length

Max length13
Median length7
Mean length6.833925
Min length5

Characters and Unicode

Total characters182869
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowSwiss
2nd rowSwiss
3rd rowSwiss
4th rowFrench
5th rowFrench

Common Values

ValueCountFrequency (%)
British 12248
45.8%
Italian 5778
21.6%
French 2415
 
9.0%
Swiss 1296
 
4.8%
German 1091
 
4.1%
American 887
 
3.3%
Austrian 788
 
2.9%
Japanese 521
 
1.9%
Irish 500
 
1.9%
Indian 424
 
1.6%
Other values (14) 811
 
3.0%

Length

2025-08-20T23:50:24.063823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
british 12248
45.6%
italian 5778
21.5%
french 2415
 
9.0%
swiss 1296
 
4.8%
german 1092
 
4.1%
american 887
 
3.3%
austrian 788
 
2.9%
japanese 521
 
1.9%
irish 500
 
1.9%
indian 424
 
1.6%
Other values (17) 888
 
3.3%

Most occurring characters

ValueCountFrequency (%)
i 35015
19.1%
t 18874
10.3%
r 18099
9.9%
s 17230
9.4%
a 17185
9.4%
h 15338
8.4%
n 13234
 
7.2%
B 12404
 
6.8%
I 6702
 
3.7%
l 6128
 
3.4%
Other values (28) 22660
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 155954
85.3%
Uppercase Letter 26837
 
14.7%
Space Separator 78
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 35015
22.5%
t 18874
12.1%
r 18099
11.6%
s 17230
11.0%
a 17185
11.0%
h 15338
9.8%
n 13234
 
8.5%
l 6128
 
3.9%
e 5456
 
3.5%
c 3363
 
2.2%
Other values (11) 6032
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
B 12404
46.2%
I 6702
25.0%
F 2415
 
9.0%
A 1684
 
6.3%
S 1420
 
5.3%
G 1092
 
4.1%
J 521
 
1.9%
M 191
 
0.7%
R 138
 
0.5%
C 81
 
0.3%
Other values (6) 189
 
0.7%
Space Separator
ValueCountFrequency (%)
78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 182791
> 99.9%
Common 78
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 35015
19.2%
t 18874
10.3%
r 18099
9.9%
s 17230
9.4%
a 17185
9.4%
h 15338
8.4%
n 13234
 
7.2%
B 12404
 
6.8%
I 6702
 
3.7%
l 6128
 
3.4%
Other values (27) 22582
12.4%
Common
ValueCountFrequency (%)
78
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 35015
19.1%
t 18874
10.3%
r 18099
9.9%
s 17230
9.4%
a 17185
9.4%
h 15338
8.4%
n 13234
 
7.2%
B 12404
 
6.8%
I 6702
 
3.7%
l 6128
 
3.4%
Other values (28) 22660
12.4%

year
Real number (ℝ)

Distinct75
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.3944
Minimum1950
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:24.118945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1956
Q11977
median1991
Q32009
95-th percentile2022
Maximum2024
Range74
Interquartile range (IQR)32

Descriptive statistics

Standard deviation19.952885
Coefficient of variation (CV)0.010019555
Kurtosis-0.93352649
Mean1991.3944
Median Absolute Deviation (MAD)16
Skewness-0.18167295
Sum53287722
Variance398.11762
MonotonicityIncreasing
2025-08-20T23:50:24.177531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1989 620
 
2.3%
1990 542
 
2.0%
1991 539
 
2.0%
1988 495
 
1.8%
2012 480
 
1.8%
2024 479
 
1.8%
1992 478
 
1.8%
1977 477
 
1.8%
1978 471
 
1.8%
1982 465
 
1.7%
Other values (65) 21713
81.1%
ValueCountFrequency (%)
1950 160
0.6%
1951 179
0.7%
1952 215
0.8%
1953 246
0.9%
1954 230
0.9%
1955 180
0.7%
1956 190
0.7%
1957 171
0.6%
1958 241
0.9%
1959 195
0.7%
ValueCountFrequency (%)
2024 479
1.8%
2023 440
1.6%
2022 440
1.6%
2021 440
1.6%
2020 340
1.3%
2019 420
1.6%
2018 420
1.6%
2017 400
1.5%
2016 462
1.7%
2015 378
1.4%

round
Real number (ℝ)

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5111925
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:24.231035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q312
95-th percentile17
Maximum24
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.070231
Coefficient of variation (CV)0.59571335
Kurtosis-0.72680562
Mean8.5111925
Median Absolute Deviation (MAD)4
Skewness0.37134076
Sum227751
Variance25.707243
MonotonicityNot monotonic
2025-08-20T23:50:24.280687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 1840
 
6.9%
6 1811
 
6.8%
7 1809
 
6.8%
5 1802
 
6.7%
3 1793
 
6.7%
8 1741
 
6.5%
1 1738
 
6.5%
4 1736
 
6.5%
9 1625
 
6.1%
10 1535
 
5.7%
Other values (14) 9329
34.9%
ValueCountFrequency (%)
1 1738
6.5%
2 1840
6.9%
3 1793
6.7%
4 1736
6.5%
5 1802
6.7%
6 1811
6.8%
7 1809
6.8%
8 1741
6.5%
9 1625
6.1%
10 1535
5.7%
ValueCountFrequency (%)
24 20
 
0.1%
23 20
 
0.1%
22 80
 
0.3%
21 142
 
0.5%
20 186
 
0.7%
19 316
 
1.2%
18 376
 
1.4%
17 571
2.1%
16 1061
4.0%
15 1200
4.5%

circuitId
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.820808
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-08-20T23:50:24.334963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median18
Q334
95-th percentile70
Maximum80
Range79
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.112002
Coefficient of variation (CV)0.80232384
Kurtosis0.51475935
Mean23.820808
Median Absolute Deviation (MAD)11
Skewness1.1486486
Sum637421
Variance365.26862
MonotonicityNot monotonic
2025-08-20T23:50:24.395808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1836
 
6.9%
6 1664
 
6.2%
9 1436
 
5.4%
13 1258
 
4.7%
7 1052
 
3.9%
20 976
 
3.6%
18 937
 
3.5%
10 935
 
3.5%
11 911
 
3.4%
70 901
 
3.4%
Other values (67) 14853
55.5%
ValueCountFrequency (%)
1 577
 
2.2%
2 412
 
1.5%
3 440
 
1.6%
4 759
2.8%
5 192
 
0.7%
6 1664
6.2%
7 1052
3.9%
8 420
 
1.6%
9 1436
5.4%
10 935
3.5%
ValueCountFrequency (%)
80 40
 
0.1%
79 60
 
0.2%
78 60
 
0.2%
77 80
 
0.3%
76 20
 
0.1%
75 40
 
0.1%
73 162
 
0.6%
71 163
 
0.6%
70 901
3.4%
69 246
 
0.9%
Distinct54
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2025-08-20T23:50:24.474997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length18.891775
Min length16

Characters and Unicode

Total characters505525
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBritish Grand Prix
2nd rowBritish Grand Prix
3rd rowBritish Grand Prix
4th rowBritish Grand Prix
5th rowBritish Grand Prix
ValueCountFrequency (%)
grand 26354
31.6%
prix 26354
31.6%
british 1873
 
2.2%
italian 1864
 
2.2%
monaco 1664
 
2.0%
german 1599
 
1.9%
belgian 1597
 
1.9%
french 1484
 
1.8%
canadian 1291
 
1.5%
spanish 1276
 
1.5%
Other values (58) 18029
21.6%
2025-08-20T23:50:24.609684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 65905
13.0%
a 58280
11.5%
56626
11.2%
n 52267
10.3%
i 49044
9.7%
d 29628
 
5.9%
G 27953
 
5.5%
P 27015
 
5.3%
x 26956
 
5.3%
e 13976
 
2.8%
Other values (41) 97875
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 364684
72.1%
Uppercase Letter 82960
 
16.4%
Space Separator 56626
 
11.2%
Decimal Number 1255
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 65905
18.1%
a 58280
16.0%
n 52267
14.3%
i 49044
13.4%
d 29628
8.1%
x 26956
7.4%
e 13976
 
3.8%
t 12365
 
3.4%
s 10139
 
2.8%
h 7490
 
2.1%
Other values (16) 38634
10.6%
Uppercase Letter
ValueCountFrequency (%)
G 27953
33.7%
P 27015
32.6%
B 5033
 
6.1%
S 4619
 
5.6%
M 3386
 
4.1%
A 3375
 
4.1%
I 2339
 
2.8%
C 1793
 
2.2%
F 1484
 
1.8%
D 1320
 
1.6%
Other values (11) 4643
 
5.6%
Decimal Number
ValueCountFrequency (%)
0 830
66.1%
5 405
32.3%
7 20
 
1.6%
Space Separator
ValueCountFrequency (%)
56626
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 447644
88.6%
Common 57881
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 65905
14.7%
a 58280
13.0%
n 52267
11.7%
i 49044
11.0%
d 29628
 
6.6%
G 27953
 
6.2%
P 27015
 
6.0%
x 26956
 
6.0%
e 13976
 
3.1%
t 12365
 
2.8%
Other values (37) 84255
18.8%
Common
ValueCountFrequency (%)
56626
97.8%
0 830
 
1.4%
5 405
 
0.7%
7 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 505445
> 99.9%
None 80
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 65905
13.0%
a 58280
11.5%
56626
11.2%
n 52267
10.3%
i 49044
9.7%
d 29628
 
5.9%
G 27953
 
5.5%
P 27015
 
5.3%
x 26956
 
5.3%
e 13976
 
2.8%
Other values (40) 97795
19.3%
None
ValueCountFrequency (%)
ã 80
100.0%

date
Date

Distinct1125
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
Minimum1950-05-13 00:00:00
Maximum2024-12-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-20T23:50:24.674189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:24.731836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

time_y
Categorical

High correlation  Imbalance 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
\N
18469 
12:00:00
2458 
13:00:00
 
1020
14:00:00
 
702
13:10:00
 
600
Other values (30)
3510 

Length

Max length8
Median length2
Mean length3.8588139
Min length2

Characters and Unicode

Total characters103258
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N

Common Values

ValueCountFrequency (%)
\N 18469
69.0%
12:00:00 2458
 
9.2%
13:00:00 1020
 
3.8%
14:00:00 702
 
2.6%
13:10:00 600
 
2.2%
06:00:00 470
 
1.8%
19:00:00 270
 
1.0%
17:00:00 268
 
1.0%
05:00:00 266
 
1.0%
07:00:00 258
 
1.0%
Other values (25) 1978
 
7.4%

Length

2025-08-20T23:50:24.787941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 18469
69.0%
12:00:00 2458
 
9.2%
13:00:00 1020
 
3.8%
14:00:00 702
 
2.6%
13:10:00 600
 
2.2%
06:00:00 470
 
1.8%
19:00:00 270
 
1.0%
17:00:00 268
 
1.0%
05:00:00 266
 
1.0%
07:00:00 258
 
1.0%
Other values (25) 1978
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 33243
32.2%
\ 18469
17.9%
N 18469
17.9%
: 16580
16.1%
1 8196
 
7.9%
2 2676
 
2.6%
3 1922
 
1.9%
4 867
 
0.8%
6 750
 
0.7%
5 634
 
0.6%
Other values (3) 1452
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49740
48.2%
Other Punctuation 35049
33.9%
Uppercase Letter 18469
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33243
66.8%
1 8196
 
16.5%
2 2676
 
5.4%
3 1922
 
3.9%
4 867
 
1.7%
6 750
 
1.5%
5 634
 
1.3%
7 606
 
1.2%
9 460
 
0.9%
8 386
 
0.8%
Other Punctuation
ValueCountFrequency (%)
\ 18469
52.7%
: 16580
47.3%
Uppercase Letter
ValueCountFrequency (%)
N 18469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84789
82.1%
Latin 18469
 
17.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33243
39.2%
\ 18469
21.8%
: 16580
19.6%
1 8196
 
9.7%
2 2676
 
3.2%
3 1922
 
2.3%
4 867
 
1.0%
6 750
 
0.9%
5 634
 
0.7%
7 606
 
0.7%
Other values (2) 846
 
1.0%
Latin
ValueCountFrequency (%)
N 18469
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33243
32.2%
\ 18469
17.9%
N 18469
17.9%
: 16580
16.1%
1 8196
 
7.9%
2 2676
 
2.6%
3 1922
 
1.9%
4 867
 
0.8%
6 750
 
0.7%
5 634
 
0.6%
Other values (3) 1452
 
1.4%

url
URL

Distinct1125
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
http://en.wikipedia.org/wiki/1954_Indianapolis_500
 
55
http://en.wikipedia.org/wiki/1953_Indianapolis_500
 
47
http://en.wikipedia.org/wiki/1989_Italian_Grand_Prix
 
39
http://en.wikipedia.org/wiki/1989_Canadian_Grand_Prix
 
39
http://en.wikipedia.org/wiki/1989_Australian_Grand_Prix
 
39
Other values (1120)
26540 
ValueCountFrequency (%)
http://en.wikipedia.org/wiki/1954_Indianapolis_500 55
 
0.2%
http://en.wikipedia.org/wiki/1953_Indianapolis_500 47
 
0.2%
http://en.wikipedia.org/wiki/1989_Italian_Grand_Prix 39
 
0.1%
http://en.wikipedia.org/wiki/1989_Canadian_Grand_Prix 39
 
0.1%
http://en.wikipedia.org/wiki/1989_Australian_Grand_Prix 39
 
0.1%
http://en.wikipedia.org/wiki/1961_Italian_Grand_Prix 39
 
0.1%
http://en.wikipedia.org/wiki/1989_Portuguese_Grand_Prix 39
 
0.1%
http://en.wikipedia.org/wiki/1989_Japanese_Grand_Prix 39
 
0.1%
http://en.wikipedia.org/wiki/1989_San_Marino_Grand_Prix 39
 
0.1%
http://en.wikipedia.org/wiki/1989_United_States_Grand_Prix 39
 
0.1%
Other values (1115) 26345
98.5%
ValueCountFrequency (%)
http 25840
96.6%
https 919
 
3.4%
ValueCountFrequency (%)
en.wikipedia.org 26759
100.0%
ValueCountFrequency (%)
/wiki/1954_Indianapolis_500 55
 
0.2%
/wiki/1953_Indianapolis_500 47
 
0.2%
/wiki/1989_Italian_Grand_Prix 39
 
0.1%
/wiki/1989_Canadian_Grand_Prix 39
 
0.1%
/wiki/1989_Australian_Grand_Prix 39
 
0.1%
/wiki/1961_Italian_Grand_Prix 39
 
0.1%
/wiki/1989_Portuguese_Grand_Prix 39
 
0.1%
/wiki/1989_Japanese_Grand_Prix 39
 
0.1%
/wiki/1989_San_Marino_Grand_Prix 39
 
0.1%
/wiki/1989_United_States_Grand_Prix 39
 
0.1%
Other values (1115) 26345
98.5%
ValueCountFrequency (%)
26759
100.0%
ValueCountFrequency (%)
26759
100.0%
Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-08-20T23:50:24.869070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length8.3221346
Min length3

Characters and Unicode

Total characters222692
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsilverstone
2nd rowsilverstone
3rd rowsilverstone
4th rowsilverstone
5th rowsilverstone
ValueCountFrequency (%)
monza 1836
 
6.9%
monaco 1664
 
6.2%
silverstone 1436
 
5.4%
spa 1258
 
4.7%
villeneuve 1052
 
3.9%
nurburgring 976
 
3.6%
interlagos 937
 
3.5%
hockenheimring 935
 
3.5%
hungaroring 911
 
3.4%
red_bull_ring 901
 
3.4%
Other values (67) 14853
55.5%
2025-08-20T23:50:25.012578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 26475
11.9%
n 21419
 
9.6%
r 19119
 
8.6%
e 16942
 
7.6%
i 16940
 
7.6%
o 15879
 
7.1%
l 12392
 
5.6%
s 11161
 
5.0%
g 10267
 
4.6%
u 9055
 
4.1%
Other values (17) 63043
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 218075
97.9%
Connector Punctuation 4592
 
2.1%
Dash Punctuation 25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 26475
12.1%
n 21419
 
9.8%
r 19119
 
8.8%
e 16942
 
7.8%
i 16940
 
7.8%
o 15879
 
7.3%
l 12392
 
5.7%
s 11161
 
5.1%
g 10267
 
4.7%
u 9055
 
4.2%
Other values (15) 58426
26.8%
Connector Punctuation
ValueCountFrequency (%)
_ 4592
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 218075
97.9%
Common 4617
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 26475
12.1%
n 21419
 
9.8%
r 19119
 
8.8%
e 16942
 
7.8%
i 16940
 
7.8%
o 15879
 
7.3%
l 12392
 
5.7%
s 11161
 
5.1%
g 10267
 
4.7%
u 9055
 
4.2%
Other values (15) 58426
26.8%
Common
ValueCountFrequency (%)
_ 4592
99.5%
- 25
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 222692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 26475
11.9%
n 21419
 
9.6%
r 19119
 
8.6%
e 16942
 
7.6%
i 16940
 
7.6%
o 15879
 
7.1%
l 12392
 
5.6%
s 11161
 
5.0%
g 10267
 
4.6%
u 9055
 
4.1%
Other values (17) 63043
28.3%
Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-08-20T23:50:25.107232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length37
Median length29
Mean length20.878471
Min length4

Characters and Unicode

Total characters558687
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSilverstone Circuit
2nd rowSilverstone Circuit
3rd rowSilverstone Circuit
4th rowSilverstone Circuit
5th rowSilverstone Circuit
ValueCountFrequency (%)
circuit 13029
 
17.5%
de 4348
 
5.8%
autódromo 2636
 
3.5%
autodromo 2631
 
3.5%
di 1836
 
2.5%
monza 1836
 
2.5%
nazionale 1836
 
2.5%
international 1790
 
2.4%
monaco 1664
 
2.2%
park 1604
 
2.2%
Other values (116) 41314
55.4%
2025-08-20T23:50:25.264101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 50452
 
9.0%
47765
 
8.5%
r 47750
 
8.5%
a 41905
 
7.5%
o 40163
 
7.2%
e 34907
 
6.2%
n 33908
 
6.1%
t 32395
 
5.8%
u 28405
 
5.1%
c 23365
 
4.2%
Other values (48) 177672
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 438629
78.5%
Uppercase Letter 69145
 
12.4%
Space Separator 47765
 
8.5%
Dash Punctuation 3148
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 50452
11.5%
r 47750
10.9%
a 41905
9.6%
o 40163
9.2%
e 34907
8.0%
n 33908
 
7.7%
t 32395
 
7.4%
u 28405
 
6.5%
c 23365
 
5.3%
l 18929
 
4.3%
Other values (22) 86450
19.7%
Uppercase Letter
ValueCountFrequency (%)
C 15591
22.5%
A 6843
9.9%
S 6352
9.2%
M 5581
 
8.1%
P 4298
 
6.2%
R 3611
 
5.2%
N 3575
 
5.2%
B 3431
 
5.0%
I 2901
 
4.2%
G 2891
 
4.2%
Other values (14) 14071
20.3%
Space Separator
ValueCountFrequency (%)
47765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 507774
90.9%
Common 50913
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 50452
 
9.9%
r 47750
 
9.4%
a 41905
 
8.3%
o 40163
 
7.9%
e 34907
 
6.9%
n 33908
 
6.7%
t 32395
 
6.4%
u 28405
 
5.6%
c 23365
 
4.6%
l 18929
 
3.7%
Other values (46) 155595
30.6%
Common
ValueCountFrequency (%)
47765
93.8%
- 3148
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 553048
99.0%
None 5639
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 50452
 
9.1%
47765
 
8.6%
r 47750
 
8.6%
a 41905
 
7.6%
o 40163
 
7.3%
e 34907
 
6.3%
n 33908
 
6.1%
t 32395
 
5.9%
u 28405
 
5.1%
c 23365
 
4.2%
Other values (42) 172033
31.1%
None
ValueCountFrequency (%)
ó 2636
46.7%
ü 976
 
17.3%
é 937
 
16.6%
í 558
 
9.9%
á 448
 
7.9%
ï 84
 
1.5%
Distinct75
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-08-20T23:50:25.366203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length8.4852199
Min length3

Characters and Unicode

Total characters227056
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSilverstone
2nd rowSilverstone
3rd rowSilverstone
4th rowSilverstone
5th rowSilverstone
ValueCountFrequency (%)
monza 1836
 
5.5%
monte-carlo 1664
 
5.0%
silverstone 1436
 
4.3%
spa 1258
 
3.8%
montreal 1052
 
3.2%
nürburg 976
 
2.9%
são 937
 
2.8%
paulo 937
 
2.8%
hockenheim 935
 
2.8%
budapest 911
 
2.7%
Other values (86) 21336
64.1%
2025-08-20T23:50:25.524021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 21482
 
9.5%
a 20589
 
9.1%
o 20333
 
9.0%
n 15825
 
7.0%
r 15197
 
6.7%
l 12397
 
5.5%
i 11829
 
5.2%
t 11730
 
5.2%
u 8611
 
3.8%
M 8021
 
3.5%
Other values (42) 81042
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 183905
81.0%
Uppercase Letter 34608
 
15.2%
Space Separator 6519
 
2.9%
Dash Punctuation 2024
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21482
11.7%
a 20589
11.2%
o 20333
11.1%
n 15825
8.6%
r 15197
 
8.3%
l 12397
 
6.7%
i 11829
 
6.4%
t 11730
 
6.4%
u 8611
 
4.7%
s 6737
 
3.7%
Other values (18) 39175
21.3%
Uppercase Letter
ValueCountFrequency (%)
M 8021
23.2%
S 6793
19.6%
C 3664
10.6%
B 2138
 
6.2%
A 1561
 
4.5%
I 1540
 
4.4%
N 1521
 
4.4%
P 1246
 
3.6%
H 1217
 
3.5%
L 1102
 
3.2%
Other values (12) 5805
16.8%
Space Separator
ValueCountFrequency (%)
6519
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2024
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 218513
96.2%
Common 8543
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21482
 
9.8%
a 20589
 
9.4%
o 20333
 
9.3%
n 15825
 
7.2%
r 15197
 
7.0%
l 12397
 
5.7%
i 11829
 
5.4%
t 11730
 
5.4%
u 8611
 
3.9%
M 8021
 
3.7%
Other values (40) 72499
33.2%
Common
ValueCountFrequency (%)
6519
76.3%
- 2024
 
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 224344
98.8%
None 2712
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 21482
 
9.6%
a 20589
 
9.2%
o 20333
 
9.1%
n 15825
 
7.1%
r 15197
 
6.8%
l 12397
 
5.5%
i 11829
 
5.3%
t 11730
 
5.2%
u 8611
 
3.8%
M 8021
 
3.6%
Other values (39) 78330
34.9%
None
ValueCountFrequency (%)
ã 977
36.0%
ü 976
36.0%
ó 759
28.0%

country
Categorical

High correlation 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Italy
2647 
USA
2011 
UK
1974 
Germany
1926 
Monaco
1664 
Other values (30)
16537 

Length

Max length13
Median length11
Mean length6.0449195
Min length2

Characters and Unicode

Total characters161756
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUK
2nd rowUK
3rd rowUK
4th rowUK
5th rowUK

Common Values

ValueCountFrequency (%)
Italy 2647
 
9.9%
USA 2011
 
7.5%
UK 1974
 
7.4%
Germany 1926
 
7.2%
Monaco 1664
 
6.2%
Belgium 1597
 
6.0%
France 1513
 
5.7%
Spain 1438
 
5.4%
Canada 1291
 
4.8%
Brazil 1223
 
4.6%
Other values (25) 9475
35.4%

Length

2025-08-20T23:50:25.592642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
italy 2647
 
9.6%
usa 2011
 
7.3%
uk 1974
 
7.2%
germany 1926
 
7.0%
monaco 1664
 
6.1%
belgium 1597
 
5.8%
france 1513
 
5.5%
spain 1438
 
5.2%
canada 1291
 
4.7%
brazil 1223
 
4.5%
Other values (28) 10176
37.1%

Most occurring characters

ValueCountFrequency (%)
a 25958
16.0%
n 12991
 
8.0%
r 11103
 
6.9%
i 9882
 
6.1%
e 8840
 
5.5%
l 8088
 
5.0%
t 6986
 
4.3%
y 6088
 
3.8%
u 5787
 
3.6%
A 5428
 
3.4%
Other values (33) 60605
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 126927
78.5%
Uppercase Letter 34128
 
21.1%
Space Separator 701
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 25958
20.5%
n 12991
10.2%
r 11103
8.7%
i 9882
 
7.8%
e 8840
 
7.0%
l 8088
 
6.4%
t 6986
 
5.5%
y 6088
 
4.8%
u 5787
 
4.6%
o 5407
 
4.3%
Other values (14) 25797
20.3%
Uppercase Letter
ValueCountFrequency (%)
A 5428
15.9%
S 4727
13.9%
U 4361
12.8%
B 3260
9.6%
I 2717
8.0%
M 2659
7.8%
K 2068
 
6.1%
G 1926
 
5.6%
C 1653
 
4.8%
F 1513
 
4.4%
Other values (8) 3816
11.2%
Space Separator
ValueCountFrequency (%)
701
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 161055
99.6%
Common 701
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 25958
16.1%
n 12991
 
8.1%
r 11103
 
6.9%
i 9882
 
6.1%
e 8840
 
5.5%
l 8088
 
5.0%
t 6986
 
4.3%
y 6088
 
3.8%
u 5787
 
3.6%
A 5428
 
3.4%
Other values (32) 59904
37.2%
Common
ValueCountFrequency (%)
701
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 25958
16.0%
n 12991
 
8.0%
r 11103
 
6.9%
i 9882
 
6.1%
e 8840
 
5.5%
l 8088
 
5.0%
t 6986
 
4.3%
y 6088
 
3.8%
u 5787
 
3.6%
A 5428
 
3.4%
Other values (33) 60605
37.5%

lat
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.949556
Minimum-37.8497
Maximum57.2653
Zeros0
Zeros (%)0.0%
Negative3141
Negative (%)11.7%
Memory size209.2 KiB
2025-08-20T23:50:25.647076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-37.8497
5-th percentile-33.0486
Q134.8431
median43.7347
Q349.2542
95-th percentile52.0786
Maximum57.2653
Range95.115
Interquartile range (IQR)14.4111

Descriptive statistics

Standard deviation25.24608
Coefficient of variation (CV)0.74363506
Kurtosis2.085716
Mean33.949556
Median Absolute Deviation (MAD)5.5959
Skewness-1.8588395
Sum908456.18
Variance637.36457
MonotonicityNot monotonic
2025-08-20T23:50:25.704307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.6156 1836
 
6.9%
43.7347 1664
 
6.2%
52.0786 1436
 
5.4%
50.4372 1258
 
4.7%
45.5 1052
 
3.9%
50.3356 976
 
3.6%
-23.7036 937
 
3.5%
49.3278 935
 
3.5%
47.5789 911
 
3.4%
47.2197 901
 
3.4%
Other values (67) 14853
55.5%
ValueCountFrequency (%)
-37.8497 577
2.2%
-34.9272 312
 
1.2%
-34.6943 448
1.7%
-33.0486 75
 
0.3%
-25.9894 506
1.9%
-23.7036 937
3.5%
-22.9756 286
 
1.1%
1.2914 318
 
1.2%
2.76083 412
1.5%
19.4042 558
2.1%
ValueCountFrequency (%)
57.2653 159
 
0.6%
53.4769 138
 
0.5%
52.8306 26
 
0.1%
52.4806 15
 
0.1%
52.3888 767
2.9%
52.0786 1436
5.4%
51.3569 374
 
1.4%
50.9894 282
 
1.1%
50.6211 57
 
0.2%
50.4372 1258
4.7%

lng
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7244155
Minimum-118.189
Maximum144.968
Zeros0
Zeros (%)0.0%
Negative8201
Negative (%)30.6%
Memory size209.2 KiB
2025-08-20T23:50:25.759705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-118.189
5-th percentile-86.2347
Q1-1.01694
median6.9475
Q314.7647
95-th percentile136.541
Maximum144.968
Range263.157
Interquartile range (IQR)15.78164

Descriptive statistics

Standard deviation57.632776
Coefficient of variation (CV)10.067888
Kurtosis0.69820892
Mean5.7244155
Median Absolute Deviation (MAD)7.96444
Skewness0.45023638
Sum153179.63
Variance3321.5368
MonotonicityNot monotonic
2025-08-20T23:50:25.942771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.28111 1836
 
6.9%
7.42056 1664
 
6.2%
-1.01694 1436
 
5.4%
5.97139 1258
 
4.7%
-73.5228 1052
 
3.9%
6.9475 976
 
3.6%
-46.6997 937
 
3.5%
8.56583 935
 
3.5%
19.2486 911
 
3.4%
14.7647 901
 
3.4%
Other values (67) 14853
55.5%
ValueCountFrequency (%)
-118.189 220
 
0.8%
-117.273 23
 
0.1%
-115.174 60
 
0.2%
-115.173 40
 
0.1%
-112.075 108
 
0.4%
-99.0907 558
2.1%
-97.6411 246
0.9%
-96.7587 26
 
0.1%
-86.2347 573
2.1%
-83.0401 191
 
0.7%
ValueCountFrequency (%)
144.968 577
2.2%
138.927 91
 
0.3%
138.617 312
 
1.2%
136.541 791
3.0%
134.221 52
 
0.2%
126.417 94
 
0.4%
121.22 362
1.4%
103.864 318
1.2%
101.738 412
1.5%
77.5331 70
 
0.3%

alt
Real number (ℝ)

High correlation 

Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.84742
Minimum-7
Maximum2227
Zeros154
Zeros (%)0.6%
Negative162
Negative (%)0.6%
Memory size209.2 KiB
2025-08-20T23:50:26.017790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-7
5-th percentile6
Q118
median153
Q3401
95-th percentile1126
Maximum2227
Range2234
Interquartile range (IQR)383

Descriptive statistics

Standard deviation409.08655
Coefficient of variation (CV)1.4776607
Kurtosis9.5036422
Mean276.84742
Median Absolute Deviation (MAD)140
Skewness2.8390667
Sum7408160
Variance167351.81
MonotonicityNot monotonic
2025-08-20T23:50:26.087551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 2104
 
7.9%
162 1836
 
6.9%
153 1595
 
6.0%
401 1258
 
4.7%
13 1052
 
3.9%
37 979
 
3.7%
578 976
 
3.6%
785 937
 
3.5%
103 935
 
3.5%
264 911
 
3.4%
Other values (56) 14176
53.0%
ValueCountFrequency (%)
-7 162
 
0.6%
0 154
 
0.6%
2 163
 
0.6%
3 336
 
1.3%
4 112
 
0.4%
5 362
 
1.4%
6 767
 
2.9%
7 2104
7.9%
8 448
 
1.7%
10 577
 
2.2%
ValueCountFrequency (%)
2227 558
2.1%
1460 506
1.9%
1126 286
 
1.1%
790 78
 
0.3%
785 937
3.5%
678 901
3.4%
676 20
 
0.1%
642 40
 
0.1%
639 60
 
0.2%
609 236
 
0.9%

race_datetime
Date

Missing 

Distinct394
Distinct (%)4.8%
Missing18469
Missing (%)69.0%
Memory size209.2 KiB
Minimum2005-03-06 14:00:00
Maximum2024-12-08 13:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-20T23:50:26.148496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:26.206599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-08-20T23:50:18.116741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.193845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.971462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.635484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.437751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.115376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.781866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.467042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.106134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.935974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.606169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.282807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.970673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.712621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.469496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.156950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.258518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.013374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.679173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.479356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.160944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.827171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.509295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.149490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.980069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.649896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.324635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.015210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.755142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.510391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.200123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.339615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.056519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.720277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.521622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.205984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.871835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.551419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.195552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.026117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.696592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.368459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.061014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.797373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.555122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.239124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.411899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.098126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.759009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.560210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.248484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.916170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.593417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.237808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.067081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.738938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.407608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.108972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.837153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.597533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.278689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.481357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.139834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.799610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.601008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.289212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.959093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.631943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.280415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.109975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.780536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.447353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.161001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.877689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.640054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.322523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.525637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.186869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.844696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.645266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.334802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.008252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.677879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.328824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.156384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.828191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.492151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.221036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.920335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.685553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.366788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.575557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.233248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.058058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.725403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.380467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.054416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.723029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.378098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.203448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.875519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.536397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.306739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.967527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.731571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.408105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.616755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.278545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.099623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.766119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.424288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.099456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.763095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.453677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.246864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.919189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.601699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.350647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.008701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.772439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.452417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.665689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.325729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.144526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.812614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.470144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.148122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.809264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.502322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.293430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.967799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.661559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.399608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.054932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.818099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.496666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.710930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.371385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.187445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.857193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.517142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.196161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.852726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.549576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.340023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.014096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.716670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.445578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.098045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.863257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.540822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.754822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.417671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.231464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.901715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.562811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.244128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.897252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.719671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.385800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.060251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.761989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.493670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.145232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.907626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.580730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.797741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.462708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.272071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.944907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.607522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.288133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.938475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.761963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.429903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.103920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.801098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.536211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.185352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.947451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.626686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.843584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.509810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.317461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.989391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.654321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.336225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.983991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.809540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.476940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.151333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.848489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.584390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.229143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.995303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.665987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.887281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.550678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.356723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.031667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.697893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.380351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.025594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.851715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.518875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.195606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.889027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.626618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.390993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.035383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.709928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:08.929109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:09.595629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:10.399098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.074147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:11.741683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:12.425540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.067226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:13.894804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:14.562758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.240434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:15.930383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:16.671051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:17.430251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-08-20T23:50:18.077673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-08-20T23:50:26.257587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
altcircuitIdconstructorIdconstructors_nationalitycountrydriverIddriver_nationalitydriver_numbergridlapslatlngpointspositionpositionOrderpositionTextraceIdrankresultIdroundstatusIdtime_yyear
alt1.0000.2830.0530.0980.8060.0220.1020.0630.035-0.0390.086-0.251-0.0570.0130.0360.030-0.0530.075-0.0800.1420.0570.242-0.189
circuitId0.2831.0000.1700.1190.7200.2530.1300.1340.010-0.007-0.025-0.162-0.0390.0440.0200.0570.2170.1330.1870.1690.0620.319-0.274
constructorId0.0530.1701.0000.4080.1680.3080.2680.3260.173-0.1050.079-0.114-0.2170.1490.2020.1750.3200.2220.298-0.1160.2560.191-0.325
constructors_nationality0.0980.1190.4081.0000.0940.2320.2670.2950.1530.1930.0870.1240.1760.1140.1190.1170.2650.1730.2660.0720.0750.1220.286
country0.8060.7200.1680.0941.0000.2360.0850.0810.0560.2390.9540.9660.0740.0290.0370.0330.2750.1010.2650.4780.0740.3990.289
driverId0.0220.2530.3080.2320.2361.0000.3390.3440.0660.0050.061-0.097-0.0690.1320.0630.1540.7060.2580.669-0.0840.0920.280-0.218
driver_nationality0.1020.1300.2680.2670.0850.3391.0000.5110.1220.1710.0990.1280.1340.0670.1090.0670.2900.1260.2910.0920.0860.0990.309
driver_number0.0630.1340.3260.2950.0810.3440.5111.0000.1220.1000.1290.1420.2520.0920.1230.0860.3530.1980.3460.1230.0890.1670.344
grid0.0350.0100.1730.1530.0560.0660.1220.1221.0000.042-0.007-0.010-0.4030.2300.2230.285-0.0330.147-0.0280.0150.1750.048-0.001
laps-0.039-0.007-0.1050.1930.2390.0050.1710.1000.0421.000-0.091-0.0800.4190.270-0.6800.2940.0850.1520.093-0.014-0.2920.1580.120
lat0.086-0.0250.0790.0870.9540.0610.0990.129-0.007-0.0911.000-0.099-0.0520.0560.0190.060-0.0120.141-0.0350.1110.0600.440-0.235
lng-0.251-0.162-0.1140.1240.966-0.0970.1280.142-0.010-0.080-0.0991.0000.0690.059-0.0390.064-0.0050.1500.0250.101-0.1010.5840.283
points-0.057-0.039-0.2170.1760.074-0.0690.1340.252-0.4030.419-0.0520.0691.0000.424-0.7840.4240.1440.2700.1610.057-0.6230.1430.226
position0.0130.0440.1490.1140.0290.1320.0670.0920.2300.2700.0560.0590.4241.0000.6051.0000.1270.1640.1260.0470.1840.0610.139
positionOrder0.0360.0200.2020.1190.0370.0630.1090.1230.223-0.6800.019-0.039-0.7840.6051.0000.650-0.0650.210-0.071-0.0100.5700.067-0.100
positionText0.0300.0570.1750.1170.0330.1540.0670.0860.2850.2940.0600.0640.4241.0000.6501.0000.1490.1660.1490.0480.4030.0610.168
raceId-0.0530.2170.3200.2650.2750.7060.2900.353-0.0330.085-0.012-0.0050.1440.127-0.0650.1491.0000.3030.9690.013-0.0770.4170.073
rank0.0750.1330.2220.1730.1010.2580.1260.1980.1470.1520.1410.1500.2700.1640.2100.1660.3031.0000.2930.1100.1180.1920.323
resultId-0.0800.1870.2980.2660.2650.6690.2910.346-0.0280.093-0.0350.0250.1610.126-0.0710.1490.9690.2931.0000.029-0.1040.3920.131
round0.1420.169-0.1160.0720.478-0.0840.0920.1230.015-0.0140.1110.1010.0570.047-0.0100.0480.0130.1100.0291.000-0.1100.3410.300
statusId0.0570.0620.2560.0750.0740.0920.0860.0890.175-0.2920.060-0.101-0.6230.1840.5700.403-0.0770.118-0.104-0.1101.0000.093-0.294
time_y0.2420.3190.1910.1220.3990.2800.0990.1670.0480.1580.4400.5840.1430.0610.0670.0610.4170.1920.3920.3410.0931.0000.420
year-0.189-0.274-0.3250.2860.289-0.2180.3090.344-0.0010.120-0.2350.2830.2260.139-0.1000.1680.0730.3230.1310.300-0.2940.4201.000

Missing values

2025-08-20T23:50:18.809384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-20T23:50:19.069629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

resultIdraceIddriverIdconstructorIdrace_numbergridpositionpositionTextpositionOrderpointslapstime_xmillisecondsfastestLaprankfastestLapTimefastestLapSpeedstatusIddriverRefdriver_numbercodeforenamesurnamedobdriver_nationalityconstructorRefconstructor_nameconstructors_nationalityyearroundcircuitIdrace_namedatetime_yurlcircuitRefcircuit_namelocationcountrylatlngaltrace_datetime
02002583364251211119.0702:13:23.68003600\N\N\N\N1farina\N\NNinoFarina1906-10-30ItalianalfaAlfa RomeoSwiss195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
12002683378651322226.070+2.68006200\N\N\N\N1fagioli\N\NLuigiFagioli1898-06-09ItalianalfaAlfa RomeoSwiss195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
22002783368651443334.070+52.08055600\N\N\N\N1reg_parnell\N\NRegParnell1911-07-02BritishalfaAlfa RomeoSwiss195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
3200288337041541464443.068\N\N\N\N\N\N12cabantous\N\NYvesCabantous1904-10-08FrenchlagoTalbot-LagoFrench195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
4200298336271541595552.068\N\N\N\N\N\N12rosier\N\NLouisRosier1905-11-05FrenchlagoTalbot-LagoFrench195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
52003083361915112136660.067\N\N\N\N\N\N13gerard\N\NBobGerard1914-01-19BritisheraERABritish195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
62003183378715111157770.067\N\N\N\N\N\N13harrison\N\NCuthHarrison1906-07-06BritisheraERABritish195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
72003283374115416148880.065\N\N\N\N\N\N15etancelin\N\NPhilippeÉtancelin1896-12-28FrenchlagoTalbot-LagoFrench195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
8200338337841056169990.064\N\N\N\N\N\N16hampshire\N\NDavidHampshire1917-12-29BritishmaseratiMaseratiItalian195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
92003483377810510201010100.064\N\N\N\N\N\N16shawe_taylor\N\NBrianShawe Taylor1915-01-28BritishmaseratiMaseratiItalian195019British Grand Prix1950-05-13\Nhttp://en.wikipedia.org/wiki/1950_British_Grand_PrixsilverstoneSilverstone CircuitSilverstoneUK52.0786-1.01694153NaT
resultIdraceIddriverIdconstructorIdrace_numbergridpositionpositionTextpositionOrderpointslapstime_xmillisecondsfastestLaprankfastestLapTimefastestLapSpeedstatusIddriverRefdriver_numbercodeforenamesurnamedobdriver_nationalityconstructorRefconstructor_nameconstructors_nationalityyearroundcircuitIdrace_namedatetime_yurlcircuitRefcircuit_namelocationcountrylatlngaltrace_datetime
26749267551144848323181111110.057\N\N46181:29.438212.56711albon23ALBAlexanderAlbon1996-03-23ThaiwilliamsWilliamsBritish20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
2675026756114485221522111212120.057\N\N41151:29.200213.13411tsunoda22TSUYukiTsunoda2000-05-11JapaneserbRB F1 TeamItalian20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
267512675711448551524151313130.057\N\N5681:27.982216.08511zhou24ZHOGuanyuZhou1999-05-30ChinesesauberSauberSwiss20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
2675226758114484011718131414140.057\N\N42111:28.604214.56811stroll18STRLanceStroll1998-10-29Canadianaston_martinAston MartinBritish20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
2675326759114486221461171515150.057\N\N56131:29.121213.32311doohan61DOOJackDoohan2003-01-20AustralianalpineAlpine F1 TeamFrench20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
2675426760114482521020141616160.057\N\N5711:25.637222.00211kevin_magnussen20MAGKevinMagnussen1992-10-05DanishhaasHaas F1 TeamAmerican20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
2675526761114485921530121717170.055\N\N52121:28.751214.2125lawson30LAWLiamLawson2002-02-11New ZealanderrbRB F1 TeamItalian20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
2675626762114482215779\NR180.030\N\N14191:29.482212.462130bottas77BOTValtteriBottas1989-08-28FinnishsauberSauberSwiss20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
2675726763114486134320\NR190.026\N\N5171:29.411212.6315colapinto43COLFrancoColapinto2003-05-27ArgentinianwilliamsWilliamsBritish20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00
2675826764114481591110\NR200.00\N\N\N0\N\N4perez11PERSergioPérez1990-01-26Mexicanred_bullRed BullAustrian20242424Abu Dhabi Grand Prix2024-12-0813:00:00https://en.wikipedia.org/wiki/2024_Abu_Dhabi_Grand_Prixyas_marinaYas Marina CircuitAbu DhabiUAE24.467254.603132024-12-08 13:00:00